CHAPTER 3 OPTIMIZED ON-DEMAND MULTICAST ROUTING...
Transcript of CHAPTER 3 OPTIMIZED ON-DEMAND MULTICAST ROUTING...
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CHAPTER 3
OPTIMIZED ON-DEMAND MULTICAST ROUTING
PROTOCOL FOR MOBILE AD-HOC NETWORK (MANET)
3.1 INTRODUCTION
Mobile Ad-hoc Network (MANET) has attracted significant
attention in research over the last two decades. In spite of the challenges in
the routing protocol design, the scope of MANET is wide due to recent
technologies like Internet of Things (IoT). Applications like Underwater
Sensor Networks, Vehicular Ad-hoc Networks, Online gaming, Classroom
communication, Battlefield communications, etc., lead to a substantial
research focus on the routing protocol design for MANET. This chapter
explores On-Demand Multicast Routing Protocol (ODMRP) which is a
routing protocol for MANET and the scope for optimization is also analyzed.
One such optimization based on the control message is proposed and its
results are evaluated.
3.2 MULTICAST ROUTING IN MANET
Multicast communication refers to the single source data to be
transmitted to multiple receiving nodes. This is aided by forming a multicast
group and destination nodes are referred by a multicast id. A lot of research in
recent years has been concentrated on providing routing functionality in
multicast applications (Royer and Perkins 1999). In MANET, the success of
data transmission depends on the characteristics of a group of hosts in a
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network. The majority applications of MANET includes rescue sites, class
rooms, conventions, emergency search, battlefields, etc., which demand
participants share information dynamically and thus multicast operations.
Therefore, multicast protocols play a major role in MANET. Multicast
protocols developed for static networks, such as Distance Vector Multicast
Routing Protocol (DVMRP) (Deering and Cheriton 1990), Multicast Open
Shortest Path First (MOSPF) (Moy 1994), Core Based Trees (CBT) (Ballardie
et al 1993) and Protocol Independent Multicast (PIM) (Deering et al 1999),
do not function very well in ad-hoc network environments because of their
continuous dynamic behaviour. One of the major drawbacks of the
above-mentioned multicast protocols is that they possess an inherently
volatile tree structure. This volatile tree structure obliges these types of
networks to continuously update their link status in response to topology
changes. Additionally, typical multicast trees usually require a link state or
distance vector global routing substructure that can result in significant packet
loss. Furthermore, continuous topology changes caused by the frequent
exchange of routing vectors or link state tables can also result in excessive
channel and processing overhead, which can significantly increase network
congestion. As a result, constraints related to bandwidth resources, power
consumption and host mobility make multicast protocol design particularly
challenging.
In response to these difficulties, several multicast routing protocols
have been proposed for use in wireless ad-hoc networks, including Ad-hoc
Multicast Routing Protocol (AMRoute) (Xie et al 2002), On-Demand
Multicast Routing Protocol (ODMRP) (Ho Bae et al 2000), Ad-hoc Multicast
Routing Protocol Utilizing Increasing Id-numberS (AMRIS) (Wu et al 1999),
Core Assisted Mesh Protocol (CAMP) (Garcia and Madruga 1999), Multicast
Ad-hoc On-Demand Distance Vector (MAODV) (Perkins 2008) and Adaptive
Demand-Driven Multicast Routing protocol (ADMR) (Jetcheva and Jhonson
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2001). However, the critical disadvantage of these topological multicast
routing algorithms is that their data delivery strategies do not guarantee
efficient transmission in highly mobile environments such as vehicular ad-hoc
networks (VANET´s).
3.2.1 Multicast Route Establishment
Multicast protocols are meant for group-oriented computing. The
nodes are connected as multicast group members and this group is formed
dynamically. Any host can either join or leave the group. The host group can
have any number of members. For forwarding the data, the host need not be a
member of the group. The route is established through two phases, the route
discovery and data forward phases. Route discovery is done by sharing of
messages. Generally these messages are request and reply messages. The
request is broadcast by the source. The intermediate and the receiving nodes
will be the recipients of this request message. The response of the request is
given only by the multicast receivers. After sending the request, the source
node waits for the reply for some predetermined time. If there is no response,
it rebroadcasts the request by incrementing the broadcast id. The intermediate
nodes maintain a multicast table. They forward the request by broadcasting.
The reply is originated by the receiver node and thus the forward path is
established.
3.2.2 Example Protocols
Researchers have proposed a variety of multicast protocols namely,
AMRIS (Wu et al 1998), Ad-hoc multicast routing AMRoute (Bommaiah
et al 1998), the Core Assisted Mesh Protocol CAMP (Garcia and Madruga ,
1999), Multicast Ad-hoc On-demand Vector, (Royer and Perkins 1999),
On-demand Multicast Routing Protocol ODMRP (Gerla et al 2000) and
Differential Destination Multicast (DDM) (Ji and Corson 2001). The
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challenges in building multicast protocols for MANET are the dynamic
environment, unpredictable network topology, limited bandwidth and limited
power. Taxonomy of the multicast protocols and the design features were
explored by Junhai et al (2008). The study concludes that all protocols have
their own advantages and disadvantages.
3.3 ON-DEMAND MULTICAST ROUTING PROTOCOL
(ODMRP)
On-Demand Multicast Routing Protocol (Gerla et al 1998) is a
protocol for routing multicast and unicast traffic through Ad-hoc wireless
mesh networks. ODMRP creates routes on demand, rather than proactively
creating routes. This suffers from a route acquisition delay, although it helps
reduce network traffic in general. To help reduce the problem of this delay,
some implementations send the first data packet along with the route
discovery packet. Due to the fact that some links may be asymmetric, the path
from one node to another is not necessarily the same as the reverse path of
these nodes.
3.3.1 Principles of Forwarding Group
ODMRP has a key concept called Forwarding Group Concept as
shown in Figure 3.1. In ODMRP, group membership and multicast routes are
established and updated by the source on demand. Similar to on-demand
unicast routing protocols, ODMRP has both a request phase and a reply
phase. When a multicast source sends packets, it uses a flooding strategy to
transmit a member advertising packet to all the members of the group. This
packet, called JOIN_DATA, which also carries the payload, is periodically
broadcast to the entire network to refresh the membership information and
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update the routes. When a node receives a non-duplicate JOIN _ DATA, it
stores the upstream node ID into the routing table and rebroadcasts the packet.
When the JOIN_DATA packet reaches a multicast receiver, the receiver
creates and broadcasts a JOIN_TABLE to its neighbours. When a node
receives a JOIN_TABLE, it verifies that the next node id of one of the entries
matches its own id. If it does, the node realizes that it is located at an
intermediate point between the source and receiver and recognizes that it must
forward the packet. It then sets the Forwarding Group Flag (FG_FLAG) and
broadcasts its own JOIN_TABLE based on matched entries. The
JOIN_TABLE is thus propagated by each forwarding group member until it
reaches the multicast source via the shortest path. This process constructs
(or updates) the routes from sources to receivers and builds a mesh of nodes.
Figure 3.1 Group formation in ODMRP
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3.3.2 Features of ODMRP
Another major feature of ODMRP is maintaining the Forward
Group on an On-Demand approach. A sender periodically floods control
messages (Join Request) only if it has data to send. Figure 3.2 depicts the
on-demand approach. All intermediate nodes set up a route to the sender
(Backward Learning). Receivers update their Member Tables when they
receive Join Requests from senders. While valid entries exist in Member
Table, Join Tables are broadcast to all neighbors periodically. Neighbors
which match the route set and refresh the FG_FLAG of FG nodes. Also they
create and forward Join Tables to their neighbors. All the Join Table
exchanges are confined within the “bubble". No explicit messages are
required to join/leave multicast group (or FG)
Figure 3.2 Join reply flow
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Figure 3.3 Join table updating
An example of Join Table Forwarding is shown in Figure 3.3. An
intermediate node builds its own Join Table and forwards it only when any of
the received tables' Next Node field matches its own id.
3.4 PERFORMANCE ANALYSIS OF ON-DEMAND
MULTICAST ROUTING PROTOCOL
As highlighted in the internet draft (Yi et al 2002), the different
data rate in a network results in different transmission range. Also the path
life time is dependent on the transmission range (Wang 2005). This thrust on
transmission range (Nuevo 2003), reveals an effort of addressing this issue
in multicast protocols. This section is intended to analyze the transmission
range effects of ODMRP with more realistic approach. The analysis is
simulated in Network Simulator GloMoSim (Zeng et al 1998) and the
results are evaluated. Global Mobile Information System Simulator
(GloMoSim) is a scalable simulation environment for large wireless and
wire line communication networks. GloMoSim uses a parallel discrete event
simulation capability provided by Parsec. It simulates networks with upto
thousand nodes linked by a heterogeneous communications capability that
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includes multicast, asymmetric communications using direct satellite
broadcasts, multihop wireless communications using ad hoc networking, and
traditional Internet Protocols.
3.4.1 Network Parameters
Analysis of impact of individual parameters of a network on the
routing performance is as essential as the focus on the design of routing
protocols. Over recent time, various MANET routing protocols have evolved.
Not all of them are capable of delivering adequate performance due to the
unique MANET characteristics. Thus, it is necessary to observe the behavior
of interaction of various network parameters and the routing protocols in
MANET. Also the depth of impact of the individual parameters needs to be
explored in order to conclude about the performance of the routing protocol.
3.4.1.1 Node mobility
The wireless topology is dynamic and unpredictable due to the
mobility variations of nodes in MANET. In the future, MANETs are expected
to be deployed in myriads of scenarios having complex node mobility and
connectivity dynamics (Bai et al 2003). The node mobility characteristics are
application specific. The average connected paths at every instant are
dependent on the mobility of the nodes which consequently affects the
performance of the routing algorithms. Thus the mobility analysis of a
protocol is essential. The effects of different mobility models on the
performance of mobile IP multicast protocols are evaluated for two mobility
metrics such as number of link changes and multicast agent density
(Xu et al 2009). In this chapter, the behavior of ODMRP with different
mobility values is analyzed.
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3.4.1.2 Network size
Network size of a network refers to the number of network nodes.
Combining it as a ratio with geographical area covered by the network, it
refers to network density parameter. Network size as well as network density
are critical parameters for coordinating the network functionalities with
distributed control mechanism. The node density has a greater impact on the
routing performance of a routing protocol. A densely populated network has a
number of possible connections between any two nodes as high. Thus, a
sparsely connected network reveals poor performance. Beyond some limit of
network density, the case is reverse and it degrades the protocol performance.
3.4.2 Simulation Results
An analysis is carried out by enabling the ODMRP protocol in
GloMoSim. Initial configuration includes network size, mobility model,
source destination assignments, number of packets to be transmitted and the
multicast members. Simulation is performed for a number of 40 nodes under a
wide range of mobility (10-100m/sec). Packets of size 512B along with
802.11 MAC protocol is communicated. Different numbers of data packets at
the source (10, 15 and 20) are tested as different iterations. A front end
interactive interface is made using programming language C to extract the stat
file from simulator. Multiple iterations are carried out with different speed
values. Simulation supports a wide range of mobility. As the real time speed
of mobile devices is limited one typical speed of real time is highlighted.
The data observed for a single speed of 10m/sec is tabulated below in Table
3.1 as a sample.
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Table 3.1 Simulated ODMRP metrics for 1000 m X 1000 m network
ItNo
Speedm/sec
NodesSeed
ValuePackets Collision
Throughput
Bits/sec
1
10S-15
R-18,29,325
S-20
R-3,3,39,7,1 129,129,129
10S-3
R-6,11,136
S-15
R-3,15,151,5,7 1753,8777,8777
10S-29
R-15,18,327
S-10
R-10,2,24,8,1 9102,87,87
Table 3.1 enumerates a sample iteration with three multicast
transmissions. Sources in all the three cases transmit number of packets of
data as 20,15 and 10 respectively. The throughput of each case has been
obtained with few collisions.
3.4.2.1 Result summary
The protocol is tested for the PDR, throughput and the number
of collisions. The inferences indicates that a smooth response on PDR and
throughput is feasible for the mobile device when it most within the speed
range of 10m/s to 40m/s. Table 3.2 summarizes average of the results. This
preliminary work on ODMRP provides an idea to fix the network parameters
while implementing our proposed algorithms.
Table 3.2 Summary of simulated ODMRP metrics
Terrain DimensionAverage PDR
%Average No. of
Collisions
AverageThroughput
(bits/sec)
1000 m X 1000 m 46 4 3427
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3.5 OPTIMIZED ON DEMAND ROUTING PROTOCOL
(O-ODMRP)
With limited power, bandwidth and frequently changing topology
as well, the multicast protocols of MANET are unable to outperform. To
overcome these limitations, various optimization algorithms are proposed
towards each of these protocols. The optimization can be introduced in
several stages of data transmission like route discovery, route reply, data
transmissions, error handling etc., Performance metrics of ODMRP like PDR
can be preserved with improved and normalized packet overhead (Oh et al
2005) by dynamic refresh rate adaption. A work of improving the resilient
property of ODMRP under node failure was suggested by Pathirana and
Kwon (2007) which provides higher PDR with minimal overheads. PDR
improvement by using multicast paths technique in ODMRP was proved by
Begdillo et al (2007) using OPNET simulator. Suitability of ODMRP for
Underwater Acoustic Networks (UAN) was also proved (Bauer et al 2010).
3.5.1 Join Query Analysis
ODMRP works with the concept of Forwarding Group (FG)
concept and Join-Query (JQ) messages. The nodes receiving JQ will react
either by setting the FG flag and relaying the JQ or by generating Join-Reply
(JR). In original ODMRP when a neighbor node receives the JQ, it will check
for merely the duplication. The nodes that are not registering for membership
will also overhear this JQ. When registered members are facing any failure
problems, route failure may occur. By making use of the neighbors who have
not registered i.e., with Non-Forwarding Group (NFG) flag set status, we can
overcome this issue of failure. The promiscuous mode of the nodes enables
them to obtain control messages of the neighbors. This property can be
exploited to implement optimization based on control messages. From the
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analysis of the JQ message, three optimization procedures are proposed in this
chapter. They are
Static threshold algorithm for non-forwarding to forwarding
mode change.
Dynamic threshold algorithm for non-forwarding to forwarding
mode change.
Power threshold algorithm to change forwarding to non-
forwarding.
3.5.2 Static Threshold Algorithm (Non-Forwarding to Forwarding)
This algorithm is executed only at the nodes which are not in the
multicast group. As per the algorithm these nodes will set a counter for a
particular sequence numbered JQ. Whenever they overhear a join query they
will just register, update the counter and discard the requests. When this
updated counter value exceeds the threshold which is initially fixed by the
user, the node will assume itself as a member of the multicast group and it
will start broadcasting the JQ. The threshold is called static because it is fixed
by the user and never changed later. In this research, the threshold is obtained
by an empirical analysis.
Figure 3.4 describes the above static algorithm of non-forwarding to
forwarding mode conversion.
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Start
Initialize Counter C; (for counting JQs)
Initialize Threshold ; (maximum no. of JQs)
If not member
{
if JQ duplicate
Increment C;
If C>
Then set as member;
}
End;
Figure 3.4 Static threshold algorithm
3.5.3 Dynamic Threshold Algorithm (Non-Forwarding to
Forwarding)
Due to static algorithm, the probability of path resilience will
increase. Simultaneously there is a probability for the reduction of individual
node’s life time. This may also lead to the overall reduction of life time of the
network. Alternatively the dynamic algorithm proposed in this work considers
an adaptive service of contributing the non-grouping requests. Otherwise,
selfishness is integrated with the decision on the service of committing
non-forwarding to forwarding mode. This adaptive procedure will offer a
tradeoff between the PDR and lifetime of the node.
The methodology used is that the user uploads a small table of
thresholds with corresponding power limits in the cache memory of the nodes.
Whenever the node has join queries updated, it immediately checks the
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thresholds. The thresholds are obtained from the cache with respect to the
current power range of the node. Figure 3.5 demonstrates the algorithm for
the above dynamic procedure.
Start
Initialize Counter C; (for counting JQs)
Initialize Thresholds 1 and 2;(maximum no. of JQs)
Fix power values;
If P1
If not member
{
if JQ duplicate
Increment C;
If C> 1
Then set as member;
}
If P2
If not member
{
if JQ duplicate
Increment C;
If C> 2
Then set as member;
}
End;
Figure 3.5 Dynamic threshold algorithm
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3.5.4 Power Threshold Algorithm (Forwarding to Non-Forwarding)
The previous sections 3.5.2 and 3.5.3 considered the repeated JQs
overheard at the non-group nodes as an inference and there are no nodes in
the multicast group to support their request. Thus, the nodes not belonging to
the multicast group, volunteer in helping the request from those JQs. There is
another inference possible from the repeated occurrence of duplicate JQs at
the nodes of multicast group. The JQ to a node may be directly sent by the
source and forwarded by other neighbors too. Thus, this JQ occurrence will
reveal the strength of neighbors around a node. If a node has a dense neighbor
set, it can compromise its JQs hoping that the neighbor will support. This will
make the node to protect its energy under critical conditions. This principle
can be executed by the node whenever the repeated JQs are exceeding a limit.
The power threshold algorithm uses this methodology at the multicast group
nodes and makes them switch from forwarding to non-forwarding mode. For
applications with critical power handling requirements, the algorithm listed in
Figure 3.6 can be used.
Start
Initialize Counter C; (for counting JQs)
Initialize Threshold ; (maximum value of power)
If member
{ if JQ duplicate
Increment C;
If C>
Then set as non member; }
End;
Figure 3.6 Power threshold algorithm
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3.6 PERFORMANCE ANALYSIS OF O-ODMRP
To evaluate all the three proposed algorithms, the ODMRP is
initially simulated and its performance is analyzed under different set of
inputs. This provides a set of empirical data which are required for our
algorithms. By using these data the proposed algorithms are developed as a
new protocol called O-ODMRP. Their performance is analyzed and compared
with the ODMRP.
3.6.1 Network Scenario
As per the summary of results in 3.4.2.1, the number of nodes are
made more than 20 and the mobility range is chosen as 10-40m/s. The
simulation is carried out in GloMoSim network simulator. The node’s
position is considered to be random. The MAC layer protocol is IEEE 802.11
and the number of packets to be sent is 40 for two multicast member group
and 30 for one member group where there will be a single source sending
packet to a multicast member group. Each group consists of three
destinations. The receivers are set with bounded SNR. Nodes follows random
way point model. Random way point model is defined as the mobility pattern
in which the nodes position changes with normally distributed velocity and
direction. Also there exists pause time between the random walks. The
empirical study provides a set of input configuration parameters as listed in
Table 3.3.
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Table 3.3 Inputs in configuration file
Parameters ValuesTerrain dimension 1000*1000No of nodes 40Node input RandomMobility 10 – 40 m/secMobility type Random Way pointSimulation time 200sec
The algorithm is evaluated for the metrics of PDR and throughput
for different network conditions. The results summary obtained from the
empirical study pertaining to our algorithms are
The static threshold for JQ at non-group nodes is 80.
The average mobility range supported is 10-40m/s.
The average number nodes in the dimension of 1000X1000
supported is 10-100 nodes
The mapping of power and JQ limits for dynamic algorithm is
as listed in Table 3.4.
Table 3.4 Threshold table
Power in mw JQ Counter0-50 4050-100 50100-150 60150-200 70200-250 80>250 90
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3.6.2 Simulation Results – Static Threshold Algorithm
During this simulation, a static threshold value of 80 is stored as an
added constant in the ODMRP routing protocol file of GloMoSim. A counter
variable is added in that routing file with initialization, updating and
finalization features. The simulation is repeated for five seed values and the
average of the results are recorded. Figures 3.7-3.9 illustrates the
observations. The comparison confirms PDR increase of 56% and throughput
increase of 125% in an average with O-ODMRP. Simultaneously an increase
in power consumption due to O-ODMRP for about 80% can be observed.
The power consume is a localized computation pertaining to the network
routing functions. Assumptions are made on the values of the transmission of
messages like Joint Query and thus the average power consumed is
calculated.
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
10 15 20 25 30 35 40
Mobility (m/s)
ODMRPO-ODMRP
Figure 3.7 Mobility vs PDR with static algorithm
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2000
3000
4000
5000
6000
7000
8000
10 15 20 25 30 35 40
Mobility (m/s)
ODMRPO-ODMRP
Figure 3.8 Mobility vs throughput with static algorithm
0
100
200
300
400
10 15 20 25 30 35 40
Mobility (m/s)
ODMRPO-ODMRP
Figure 3.9 Mobility vs power consumed with static algorithm
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3.6.3 Simulation Results – Dynamic Threshold Algorithm
Here a set of power values and corresponding thresholds as given in
Table 3.8 are stored in the cache memory of routing protocol. Similar to static
procedure the counter value is initialized. The updating module makes the
counter value change adaptively. The protocol is added with a function to
retrieve the current power status of the node. These changes in the protocol
are bundled as O-ODMRP dynamic and the results obtained in the simulator
are compared with the O-ODMRP static. Figures 3.10-3.12 explains the
behavior of dynamic O-ODMRP. The observation summarizes that, by this
dynamic algorithm there is about 3% reduction of both PDR and throughput
than static algorithm. Also the power consumption in dynamic algorithm is
reduced by about 29% from that of static algorithm.
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
10 15 20 25 30 35 40Mobility (m/s)
Dynamic algorithmStatic algorithm
Figure 3.10 Mobility vs PDR with static and dynamic algorithms
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7200
7400
7600
7800
8000
10 15 20 25 30 35 40Mobility (m/s)
Dynamic…Static…
Figure 3.11 Mobility vs throughput with static and dynamic algorithms
200
250
300
350
10 15 20 25 30 35 40Mobility (m/s)
Dynamicalgorithm
Figure 3.12 Mobility vs power consumed with static and dynamicalgorithms
The entire simulation studies are observed under various mobility
values. The lower ranges are suitable for vehicular networks and thus may
confine with moving vehicles of defense. Whereas the higher range is
imagined for military services while in flight.
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3.6.4 Simulation Results – Power Threshold Algorithm
This algorithm includes a threshold similar to that, which occurs in
static procedure; but the threshold is added with the multicast nodes
functionalities in the routing protocol. Like the other two procedures, a
counter is initialized, updated and finalized. The protocol includes a function
which changes the status of the node from forwarding mode to non-
forwarding mode if the threshold exceeds. The average power consumption is
analyzed and plotted against that of ODMRP, as illustrated by Figures 3.13-
3.15. The interesting observation is that the PDR and throughput remains
same at reduced power consumption of about 2%. Thus, energy conservation
applications can make use of the power threshold algorithm for multicast
routing.
0.70.720.740.760.78
0.80.820.840.860.88
0.9
10 15 20 25 30 35 40Mobility (m/s)
Without PowerSaving algorithmWith PowerSaving algorithm
Figure 3.13 Mobility vs PDR with and without power threshold
algorithm
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5000
5500
6000
6500
7000
10 15 20 25 30 35 40Mobility (m/s)
Without PowerSaving algorithmWith PowerSaving algorithm
Figure 3.14 Mobility vs throughput with and without power threshold
algorithm
90
95
100
105
110
10 15 20 25 30 35 40
Mobility (m/s)
Without PowerSaving algorithmWith Power Savingalgorithm
Figure 3.15 Mobility vs power consumed with and without power
threshold algorithms
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3.7 SUMMARY
A through experiment of ODMRP protocol has been carried out
and the scope of performance improvements has been analyzed. The adoptive
approaches of changing forwarding mode to non-forwarding mode and
non-forwarding mode to forwarding mode have been verified. The result
benefits out of forwarding to non-forwarding is mainly pertaining to the self
power of the node. Non-forwarding to forwarding provides benefits like
increase in PDR and throughput with reduction in power as well. The graphs
obtained confirm the real time speed suitability of the proposed algorithm.
The inferences helped in designing base protocol for our minimal
network coding algorithms. The various choices of enhancements are deeply
observed and an O-ODMRP which uses static threshold algorithm is
finalized. Thus, the minimal network coding algorithms proposed in the later
chapters use the base protocol as static threshold based O-ODMRP.